Sparse spectrum fitting algorithm using signal covariance matrix reconstruction and weighted sparse constraint

• Published in: Multidimensional systems and signal processing Vol. 33; no. 3; pp. 807 - 817
• Main Authors: Wang, Hao, Zhang, Hong, Ma, Qiming
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2022; Springer Nature B.V
• Subjects: Adaptive algorithms; Artificial Intelligence; Circuits and Systems; Covariance matrix; Electrical Engineering; Engineering; Interference; Reconstruction; Signal,Image and Speech Processing; Sparse spectrum fitting; Weighted sparse constraint; Iterative adaptive approach; Signal covariance matrix reconstruction; Direction of arrival (DOA) estimation
• ISSN: 0923-6082; 1573-0824
• DOI: 10.1007/s11045-021-00811-x

Although sparse spectrum fitting algorithm has good multi-target resolution, the performance is decreased under the influence of strong interference. In response to this problem, a sparse spectrum fitting algorithm using signal covariance matrix reconstruction and weighted sparse constraint is proposed. The algorithm uses iterative adaptive approach (IAA) to estimate spatial spectrum and divide signal region. The signal region is integrated to reconstruct the signal covariance matrix. On the other hand, the l 1 norm constraint is weighted in different angles by the inverse spatial spectrum estimated by IAA. Finally, the spatial spectrum of the signal region is fitted to detect and resolve the weak targets. Simulation and theoretical analysis show that the proposed algorithm has outform performance under the influence of strong interference.